Game 3

"In Games 1 and 2 you didnโ€™t have to do anything, but this time you do. There is one back-door path from X to Y, Xโ†Bโ†’Y, which can only be blocked by controlling for B. If B is unobservable, then there is no way of estimating the effect of X on Y without running a randomized controlled experiment. Some (in fact, most) statisticians in this situation would control for A, as a proxy for the unobservable variable B, but this only partially eliminates the confounding bias and introduces a new collider bias." (Pearl, p. 160)

Game 3 in BayesiaLab

As with the earlier games, we encode Game 3 as a causal Bayesian network graph:

BoW_BackDoor3.xbl

  • Again, the probabilities are fictitious and irrelevant.

  • We select Main Menu > Analysis > Visual > Graph > Influence Paths to Target to analyze the paths from X to Y.

  • Given the presence of a noncausal path (highlighted in pink), it becomes clear that we need to control for B to block that path.

  • Here, "fixing the probabilities" of B are a practical way of controlling for that variable. Note that the states and the values of the variable are irrelevant.

  • Now, after controlling for B, only one causal path remains, highlighted in blue, which allows us to estimate the effect of X and Y.

  • However, if B were unobservable ("not observable" or "hidden" in BayesiaLab terminology), some statisticians would perhaps propose to control for A as a proxy of B.

  • Let's try that scenario as well. We are now fixing A while leaving B "open."

  • The Influence Path Analysis reveals that controlling for proxy A does not achieve our objective.

  • Not only does it not block the noncausal path Xโ†Bโ†’Y, controlling for A introduces an additional noncausal path Xโ†’A โ†Bโ†’Y, i.e., another bias that prevents us from estimating the effect of X on Y.

  • This phenomenon is known as "collider bias," as it is produced by conditioning on a collider, such as A.

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